code: def separate_heads(self, inputs, batch_size):
      inputs = torch.reshape(
          inputs, (batch_size, -1, self.num_heads, self.projection_dim)
      )
      return torch.permute(inputs, (0, 2, 1, 3))
  
imports: import logging
  import torch
  from torch import nn
  from torch.nn import functional
  from ludwig.utils.torch_utils import get_activation, LudwigModule
label_desc: Use image node_id 3 for calling the MultiHeadSelfAttention obj's underlying member method code with example usage: obj.separate_heads(inputs, batch_size) and returns: torch